CN106127126B - The recognition methods of radical driving behavior based on three anxious data - Google Patents
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Abstract
The present invention proposes the recognition methods of the radical driving behavior based on three anxious data, and the radical degree of each route driving behavior is determined according to radical index.Wherein the value of motor vehicle direction of advance and transverse acceleration is calculated the following steps are included: extract 3-axis acceleration data all in some route in method；The identification suddenly behaviors such as acceleration, anxious deceleration, zig zag, acceleration, deceleration, turning, and count the frequency occurred in single route；Radical exponential model is built, estimates the radical index of route, determines the radical degree of the route driving behavior, assesses driving behavior；The method implemented according to the present invention, to driver, can timely learning special time period driving behavior and can distinguish the behavioral differences of different route, provide data supporting conducive to the improvement for driving behavior next time；The data are also capable of providing to insurance company simultaneously, are provided data for insurance reform and are supported there is very big market application prospect.
Description
Technical field
The invention belongs to vehicle monitoring fields, and in particular to the recognition methods of the radical driving behavior based on three anxious data.
Background technique
With the rapid development of economy, China has evolved into as one of maximum automobile market in the whole world, automobile
Ownership is larger, the tremendous growth potential of automobile.Traffic accident rate also constantly increases while automobile quantity increases, and is
Traffic accident can be reduced, automobile active safety intelligent terminal occurs, it can to the danger encountered in driving procedure
Danger gives warning in advance.
Current method for early warning has the method that acceleration modeling identifies radical driving behavior that is directly based upon, and needs continuous
Acceleration information is all acquired and is saved, and with the increase of driving time, the data volume for needing to store can be very huge, and
When data volume is less, accuracy decline is very fast；Also there is the method combined based on different driving modes to identify radical driving behavior,
Existing five big driving mode be speed, acceleration, with speeding, thread-changing, turning, assessed according to this five kinds of driving modes driving row
To need additional acquisition speed and relative distance data, single acceleration transducer is unable to satisfy needs, it is meant that data obtain
Take the raising of cost.
Summary of the invention
The purpose of the present invention is three the anxious acceleration paid close attention to the most by people, anxious deceleration, zig zag factors quickly to know
Not radical driving behavior not only has relatively high precision, and the factor of use is few, and the anxious data of three be calculated and energy
The formulation for being supplied to insurance company enough as danger expense standard provides data supporting, so that data supporting is provided for danger expense reform, in order to
A kind of recognition methods for the radical driving behavior based on three anxious data for solving the above problems, and proposing.
The technical scheme adopted by the invention is that:
1, the recognition methods of the radical driving behavior based on three anxious data, comprising the following steps:
S1 extracts all 3-axis acceleration data of the acceleration transducer in some route based on automobile intelligent terminal,
Noise reduction, reference axis conversion pretreatment are carried out, according to the angle of sensor and horizontal planeBe calculated motor vehicle direction of advance and
The value of lateral linear accelerations；
S2 identifies anxious acceleration in combination with threshold value according to motor vehicle direction of advance and the value of lateral linear accelerations, suddenly subtracts
Speed zig zag, accelerates, slows down, turning behavior；
S3 is counted and is suddenly accelerated in single route, anxious to slow down, and is taken a sudden turn, and is accelerated, and is slowed down, the number that turning behavior occurs；
S4 builds radical exponential model:
In above formula:
0 < α=β=δ≤100；
F (k1)=exp (a*k1), a=(V2-V1)/t, 0 < k1≤1, V1 are speed before accelerating, and V2 is speed after accelerating,
T-route or total trip time；
The radical index of SJ_Index-；
Anxious acceleration-JJS, anxious deceleration-JSC, zig zag-JZW,
Acceleration-JS, deceleration-SC, turning-ZW；
∑ JJSi has to go to the toilet for reach, and accelerated events are total, and ∑ JSj is accelerated events sum in reach, similarly use
In ∑ JSCi, ∑ SCj, ∑ JZWi, ∑ ZWj；
S5 estimates the radical index of each route according to radical exponential model, while radical to route based on preset threshold
Index is classified, and feeds back to driver；
In the step S1, the angle of sensor and horizontal planeAccording to motor vehicle when the ground of relative level is static
Sensor y-axis, z-axis read ayIt is quiet、azIt is quiet, it is calculated with trigonometric function,(ayIt is quiet/azIt is quiet)。
It in the step S1, is calculated with trigonometric function, show that the value of motor vehicle direction of advance linear acceleration isThe value of lateral linear accelerations is axIt drives, axIt drives、ayIt drives、azIt drivesFor the 3-axis acceleration in route.
In the step S2, motor vehicle direction of advance and each 50 data of lateral linear accelerations are one group, need first to obtain
The first two of every group of each acceleration of motor vehicle ten, intermediate ten, last 20 several average values and variance out, and it is intermediate
Ten several maximum values and minimum value:
Average value: Mean (a (i:i+19)), Mean (a (i+20:i+29)), Mean (a (i+30:i+49)),
Variance: VAR (a (i:i+19)), VAR (a (i+20:i+29)), VAR (a (i+30:i+49)),
Intermediate ten several maximum values and minimum value: Max (a (i+20, i+29)), Min (a (i+20:i+29)),
Absolute value: Abs (Mean (a (i:i+19))), Abs (Mean (a (i+20:i+29))),
Abs(Mean(a(i+30:i+49)))
The anxious identification condition for accelerating behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))<Mean(a(i+20:i+29)),
Max (a (i+20, i+29)) >=3, Min (a (i+20:i+29)) > 1,
VAR(a(i:i+19))>VAR(a(i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is direction of advance linear acceleration,
The identification condition of the urgency deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean (a (i+30:i+49)) > Mean (a (i+20:i+29)),
(a (i+20, i+29)) < (- 2) Max, (a (i+20:i+29))≤(- 4) Min,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of the zig zag behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Min (a (i+20:i+29))) >=1, Abs (Max (a (i+20:i+29))) >=2.2,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is lateral linear accelerations,
The identification condition of the acceleration behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29))；
Mean(a(i+30:i+49))<Mean(a(i+20:i+29))；
Min(a(i+20:i+29))>0.25；Mean (a (i+20:i+29)) >=0.4；
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of the deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))>Mean(a(i+20:i+29)),
(a (i+20, i+29))≤(- 0.5) Max, (a (i+20:i+29))≤(- 1.2) Mean,
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of the turning behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Mean (a (i+20:i+29))) >=0.9, Abs (Min (a (i+20:i+29))) >=0.5,
Abs (Max (a (i+20:i+29))) >=1.3, VAR (a (i:i+19)) > VAR (a (i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is lateral linear accelerations.
In the step S4, α=β=δ=100/3, k1=0.5.
In the step S5, the radical index of route is classified according to preset threshold, by worst to being preferably divided into four
Grade, the preset threshold for dividing four grades is respectively 100,80,60.
The present invention compared with the existing technology has the following advantages that and effect:
1, three are walked rapidly and is combined for frequency with common acceleration, deceleration, turning behavior frequency number data, it is only necessary to pass through algorithm
It walks rapidly for and accelerates, deceleration, the preservation of turning behavior event acquisition three, the amount of data stored, Neng Gouyou can be reduced significantly
The radical degree of effect ground quantization driver itself operation can be commented in conjunction with three anxious frequencies in the unit time from two dimensions
Estimate the radical degree of driving behavior, there is relatively high precision.
2, urgency acceleration, the anxious deceleration, the radical degree of zig zag three behaviors modeling analysis driving more paid close attention to from everybody, are adopted
It is realized with the less factor and the high efficiency of radical driving behavior is identified, it is only necessary to which single acceleration transducer can meet
It is required that cost is relatively low for data acquisition.
3, it three walks rapidly insensitive to the decline of frequency acquisition for the acquisition precision of event, reduces to equipment performance and equipment
The requirement of memory space.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.
Fig. 1 is method flow schematic diagram of the invention.
Fig. 2 is that the mounting means of acceleration transducer of the invention and corresponding angle calculate schematic diagram.
Label declaration:
1 front windshield
2 acceleration transducers
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
Embodiment 1: as shown in Fig. 1 to 2, sensor 2 is mounted on front windshield 1, the angle of sensor 2 and horizontal planeIt first in motor vehicle when the ground of relative level is static, is calculated by the three axis reading of sensor 2, y-axis and z-axis reading
ayIt is quietAnd azIt is quietRespectively gravity is along the downward component with vertical windshield 1 of windshield 1, the angle of sensor 2 and horizontal plane(ayIt is quiet/azIt is quiet), it saves in the sensor.
All 3-axis acceleration data axs of the acceleration transducer 2 in reach are extracted based on automobile intelligent terminalIt drives、
ayIt drives、azIt drives, 2 sample frequency of acceleration transducer is 10Hz, first carries out noise reduction, reference axis conversion preprocessing process, then basis
The angle of sensor 2 and horizontal planeThe value that motor vehicle direction of advance linear acceleration is calculated isThe value of lateral linear accelerations is axIt drives。
According to the value of motor vehicle direction of advance and lateral linear accelerations, motor vehicle direction of advance and lateral linear accelerations
Each 50 data are one group, calculate every group of direction of advance linear acceleration, lateral linear accelerations, direction of advance linear acceleration
And the first two of lateral linear accelerations ten, intermediate ten, last 20 several average values and variance, and intermediate ten numbers
Maximum value and minimum value:
Average value: Mean (a (i:i+19)), Mean (a (i+20:i+29)), Mean (a (i+30:i+49)),
Variance: VAR (a (i:i+19)), VAR (a (i+20:i+29)), VAR (a (i+30:i+49)),
Intermediate ten several maximum values and minimum value: Max (a (i+20, i+29)), Min (a (i+20:i+29)),
Absolute value: Abs (Mean (a (i:i+19))), Abs (Mean (a (i+20:i+29))),
Abs (Mean (a (i+30:i+49))),
It carries out identifying anxious acceleration then in conjunction with threshold value, anxious deceleration, zig zag, accelerate, deceleration, turning behavior, while will knowledge
Not Chu it is anxious accelerate, it is anxious slow down, zig zag, accelerate, slow down, turning behavior acquire and is saved in and stores in equipment.
The anxious identification condition for accelerating behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))<Mean(a(i+20:i+29)),
Max (a (i+20, i+29)) >=3, Min (a (i+20:i+29)) > 1,
VAR(a(i:i+19))>VAR(a(i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is direction of advance linear acceleration,
The identification condition of anxious deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean (a (i+30:i+49)) > Mean (a (i+20:i+29)),
(a (i+20, i+29)) < (- 2) Max, (a (i+20:i+29))≤(- 4) Min,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of zig zag behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Min (a (i+20:i+29))) >=1, Abs (Max (a (i+20:i+29))) >=2.2,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is lateral linear accelerations,
The identification condition of acceleration behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29))；
Mean(a(i+30:i+49))<Mean(a(i+20:i+29))；
Min(a(i+20:i+29))>0.25；Mean (a (i+20:i+29)) >=0.4；
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))>Mean(a(i+20:i+29)),
(a (i+20, i+29))≤(- 0.5) Max, (a (i+20:i+29))≤(- 1.2) Mean,
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)),
Wherein a is direction of advance linear acceleration,
The identification condition of turning behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Mean (a (i+20:i+29))) >=0.9, Abs (Min (a (i+20:i+29))) >=0.5,
Abs (Max (a (i+20:i+29))) >=1.3, VAR (a (i:i+19)) > VAR (a (i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is lateral linear accelerations.
The number that various actions occur in the route, including anxious acceleration are counted, it is anxious to slow down, it takes a sudden turn, accelerates, slow down, turn
It is curved, obtain ∑ JJSi, ∑ JSj, ∑ JSCi, ∑ SCj, ∑ JZWi, ∑ ZWj；
Based on the urgency acceleration obtained, suddenly deceleration, zig zag, acceleration, deceleration, parameter of turning build radical exponential model, such as
Under
α=β=δ=100/3, f (k1)=exp (a*k1), a=(V2-V1)/t, k1=0.5, V1 are speed before accelerating,
V2 is speed after accelerating.
The radical index of the route is estimated according to radical exponential model, while being classified based on preset threshold, is divided into
Level Four, it is worst to best classification thresholds be respectively 100,80,60, according to income index grade assign drive title, with more
Visually characterize driver driving behavior, according to radical index be respectively from high to low " hell driving ", " road anger disease patient ",
" chief crewman of a wooden boat ", " stablize Mr. ", then feeds back to driver for result, can this section of route of timely learning driving behavior
And the behavioral difference of different route can be distinguished, be conducive to provide data supporting for the improvement of next driving behavior.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (6)
1. the recognition methods of the radical driving behavior based on three anxious data, which comprises the following steps:
S1 extracts all 3-axis acceleration data of the acceleration transducer in some route based on automobile intelligent terminal, carries out
Noise reduction, reference axis conversion pretreatment, according to the angle of sensor and horizontal planeMotor vehicle direction of advance and transverse direction is calculated
The value of acceleration；
S2 identifies anxious acceleration, anxious deceleration, racing in combination with threshold value according to the value of motor vehicle direction of advance and transverse acceleration
Curved, acceleration is slowed down, turning behavior；
S3 is counted and is suddenly accelerated in single route, anxious to slow down, and is taken a sudden turn, and is accelerated, and is slowed down, the number that turning behavior occurs；
S4 builds radical exponential model:
In above formula:
0 < α=β=δ≤100；
F (k1)=exp (a*k1), a=(V2-V1)/t, 0 < k1≤1, V1To accelerate preceding speed, V2For speed after acceleration, t-trip
Journey or total trip time；
The radical index of SJ_Index-；
Anxious acceleration-JJS, anxious deceleration-JSC, zig zag-JZW,
Acceleration-JS, deceleration-SC, turning-ZW；
∑JJSiFor reach have to go to the toilet accelerated events sum, ∑ JSjFor accelerated events sum in reach, it to be similarly used for ∑
JSCi, ∑ SCj, ∑ JZWi, ∑ ZWj；
S5 estimates the radical index of single route according to radical exponential model, while based on preset threshold to the radical index of route
It is classified, and feeds back to driver.
2. the recognition methods of the radical driving behavior according to claim 1 based on three anxious data, which is characterized in that described
In step S1, the angle of sensor and horizontal planeAccording to sensor y-axis of the motor vehicle when the ground of relative level is static, z
Axis reads ayIt is quiet、azIt is quiet, it is calculated with trigonometric function,
3. the recognition methods of the radical driving behavior according to claim 1 based on three anxious data, which is characterized in that described
It in step S1, is calculated with trigonometric function, show that the value of motor vehicle direction of advance acceleration is
The value of transverse acceleration is axIt drives, axIt drives、ayIt drives、azIt drivesFor the 3-axis acceleration in route.
4. the recognition methods of the radical driving behavior according to claim 1 based on three anxious data, which is characterized in that described
In step S2, motor vehicle direction of advance and each 50 data of transverse acceleration are one group, need first to obtain each acceleration of motor vehicle
Spend the first two ten of every group, intermediate ten, last 20 several average values and variance, and intermediate ten several maximum values and
Minimum value:
Average value: Mean (a (i:i+19)), Mean (a (i+20:i+29)), Mean (a (i+30:i+49)),
Variance: VAR (a (i:i+19)), VAR (a (i+20:i+29)), VAR (a (i+30:i+49)),
Intermediate ten several maximum values and minimum value: Max (a (i+20, i+29)), Min (a (i+20:i+29)),
The anxious identification condition for accelerating behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))<Mean(a(i+20:i+29)),
Max (a (i+20, i+29)) >=3, Min (a (i+20:i+29)) > 1,
VAR(a(i:i+19))>VAR(a(i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is direction of advance linear acceleration,
The identification condition of the urgency deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean (a (i+30:i+49)) > Mean (a (i+20:i+29)),
(a (i+20, i+29)) < (- 2) Max, (a (i+20:i+29))≤(- 4) Min,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein
A is direction of advance linear acceleration,
The identification condition of the zig zag behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Min (a (i+20:i+29))) >=1, Abs (Max (a (i+20:i+29))) >=2.2,
VAR (a (i:i+19)) > VAR (a (i+20:i+29)), VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein
A is lateral linear accelerations,
The identification condition of the acceleration behavior are as follows:
Mean(a(i:i+19))<Mean(a(i+20:i+29))；
Mean(a(i+30:i+49))<Mean(a(i+20:i+29))；
Min(a(i+20:i+29))>0.25；Mean (a (i+20:i+29)) >=0.4；
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein
A is direction of advance linear acceleration,
The identification condition of the deceleration behavior are as follows:
Mean(a(i:i+19))>Mean(a(i+20:i+29)),
Mean(a(i+30:i+49))>Mean(a(i+20:i+29)),
(a (i+20, i+29))≤(- 0.5) Max, (a (i+20:i+29))≤(- 1.2) Mean,
VAR(a(i:i+19))>VAR(a(i+20:i+29))；VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein
A is direction of advance linear acceleration,
The identification condition of the turning behavior are as follows:
Abs(Mean(a(i:i+19)))<Abs(Mean(a(i+20:i+29))),
Abs(Mean(a(i+30:i+49)))<Abs(Mean(a(i+20:i+29))),
Abs (Mean (a (i+20:i+29))) >=0.9, Abs (Min (a (i+20:i+29))) >=0.5,
Abs (Max (a (i+20:i+29))) >=1.3, VAR (a (i:i+19)) > VAR (a (i+20:i+29)),
VAR (a (i+30:i+49)) > VAR (a (i+20:i+29)), wherein a is lateral linear accelerations.
5. the recognition methods of the radical driving behavior according to claim 1 based on three anxious data, which is characterized in that described
In step S4, α=β=δ=100/3, k1=0.5.
6. the recognition methods of the radical driving behavior according to claim 1 based on three anxious data, which is characterized in that described
In step S5, the radical index of route is classified according to preset threshold, by worst to four grades are preferably divided into, divides four
The preset threshold of grade is respectively 100,80,60.
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| CN108304594B (en) * | 2017-01-11 | 2022-12-13 | 厦门雅迅网络股份有限公司 | Method for judging driving stability of automobile based on speed and gyroscope data |
| CN107067724B (en) * | 2017-05-04 | 2019-10-11 | 陕西科技大学 | Smart city vehicle driving stock assessment system |
| CN107215307A (en) * | 2017-05-24 | 2017-09-29 | 清华大学深圳研究生院 | Driver identity recognition methods and system based on vehicle sensors correction data |
| CN110225446B (en) * | 2018-03-01 | 2021-01-29 | 北京嘀嘀无限科技发展有限公司 | System, method and device for identifying driving behavior and storage medium |
| CN110389577B (en) * | 2018-04-17 | 2022-04-01 | 北京三快在线科技有限公司 | Method and device for determining driving style |
| CN109733303A (en) * | 2019-01-14 | 2019-05-10 | 长安大学 | A kind of worried abnormal driving state identification method of hurrying on a journey of commercial vehicle driver |
| CN110171361B (en) * | 2019-06-17 | 2022-09-23 | 山东理工大学 | Automobile safety early warning method based on emotion and driving tendency of driver |
| CN114264486B (en) * | 2021-12-22 | 2024-04-16 | 郑州天迈科技股份有限公司 | Vehicle three-emergency detection method based on low-cost sensor |
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| JP5430454B2 (en) * | 2010-03-12 | 2014-02-26 | Ｕｄトラックス株式会社 | Safe driving promotion system |
| US9198575B1 (en) * | 2011-02-15 | 2015-12-01 | Guardvant, Inc. | System and method for determining a level of operator fatigue |
| US9751534B2 (en) * | 2013-03-15 | 2017-09-05 | Honda Motor Co., Ltd. | System and method for responding to driver state |
| CN104732785A (en) * | 2015-01-09 | 2015-06-24 | 杭州好好开车科技有限公司 | Driving behavior analyzing and reminding method and system |
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